1. Field of the Invention
The present invention relates to artificially intelligent elevator systems and, more particularly, to such systems using artificial intelligence for predicting crowds at elevator floors.
2. Description of the Related Art
Arrangements for predicting crowds at predetermined elevators floors and dispatching elevator cars to such floors are known. See, for example, commonly-owned U.S. Pat. Nos. 4,838,384; 4,846,311; 5,022,497; 5,024,295 and 5,035,302 which are all hereby incorporated by reference. The present inventor has developed arrangements for dispatching elevator cars responsive to traffic predictions. See, for example, U.S. patent application No. 07/580,888 filed Sep. 11, 1990 previously incorporated by reference. These various arrangements utilize a plurality of parameters for a predetermined floor to predict (e.g., periodically) a crowd for a particular short time interval. Such parameters include, for example, passenger boarding counts, passenger deboarding counts, hall calls and car calls for that floor. Such parameters are substantially continuously manipulated and stored by suitable programming within a microcomputer to produce both "real time" and "historic" databases which are utilized to predict crowds. The crowd predictions are then utilized by the elevator system to improve service to floors for which a crowd is predicted. The predictions are based on known prediction or forecasting techniques, such as single exponential smoothing and/or linear exponential smoothing discussed in Forecasting Methods and Applications by Spiro Makridakis and Steven C. Wheelwright (John Wiley and Sons, Inc., 1978) particularly in section 3.6: "Linear Exponential Smoothing." Linear exponential smoothing is based on Brown's one-parameter linear exponential smoothing of the Makridakis and Wheelwright text (see page 61 et seq.) and is represented by the following equation: EQU P(t+m)=2S'(t)-S"(t)+Am/(1-A) {S'(t)-S"(t)}
where EQU S'(t)=AX(t)+(1-A)S'(t-1) EQU S'(0)=X(0) EQU S"(t)=AS'(t)+(1-A)S"(t-1) EQU S"(0)=X(0)
and
P(t+m) is the prediction for "m" intervals now, PA1 S'(t) is a single smoothing value, PA1 S"(t) is a double smoothing value, PA1 A is a weighing factor and is a pure number, for example, two-tenths (0.2), PA1 m is the number of intervals ahead to be predicted, which could be, for example, two intervals, with an exemplary time interval being a one minute time period, PA1 x(o) is an initial value of the parameter being predicted, and PA1 x(t) is an observed value of the parameter being predicted at time t.
Further discussion of these prediction techniques is set forth in the commonly-owned U.S. patents and applications previously incorporated by reference. The crowd predicting and elevator dispatching arrangements discussed above have improved elevator system efficiency, but they have not proven to be entirely satisfactory. Although a crowd is predicted for a short time interval (e.g., several minutes) for a particular or predetermined floor, that crowd may not exist in real time. Dispatching elevator cars to a floor for which a crowd is predicted is inefficient if such crowd does not exist in real time.
Accordingly, the present invention employs an artificially intelligent (AI) supervisor to monitor at least one condition (e.g., load weight) of a first elevator car dispatched to a predetermined floor at which a crowd is predicted and to control the remainder of cars assigned to that floor dependent upon the monitored condition.
It is a principal object of the present invention to increase the efficiency of artificially intelligent elevator systems.
It is a further object of the present invention to employ real time data from one car servicing a particular floor during a short time interval to control the assignment of other cars to that floor during that interval.
Further and still other objects of the present invention will become more readily apparent in view of the following detailed description when taken in conjunction with the accompanying drawing, in which: